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2009 WRI World Congress on Computer Science and Information Engineering
A Robust Approach of Sonar Image Feature Detection and Matching
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
| ASCII Text | x | ||
| Shoudong Shi, Demin Xu, "A Robust Approach of Sonar Image Feature Detection and Matching," Computer Science and Information Engineering, World Congress on, vol. 6, pp. 523-527, 2009 WRI World Congress on Computer Science and Information Engineering, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/CSIE.2009.329, author = {Shoudong Shi and Demin Xu}, title = {A Robust Approach of Sonar Image Feature Detection and Matching}, journal ={Computer Science and Information Engineering, World Congress on}, volume = {6}, year = {2009}, isbn = {978-0-7695-3507-4}, pages = {523-527}, doi = {http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.329}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Computer Science and Information Engineering, World Congress on TI - A Robust Approach of Sonar Image Feature Detection and Matching SN - 978-0-7695-3507-4 SP523 EP527 A1 - Shoudong Shi, A1 - Demin Xu, PY - 2009 KW - Sonar Image Feature KW - MMWT KW - Maximum Common Subgraph KW - Matching VL - 6 JA - Computer Science and Information Engineering, World Congress on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.329
This paper is concerned with Modulus Maximum of Wavelet Transform (MMWT) and a graph theoretic method. The methods are applicable to extracting features of seafloor sonar image and data association problems. We will first get image’s modulus and modulus’ direction matrix by MMWT method. And according to calculating modulus’ threshold, obtain the geometric features of the image or the point features. Then calculate geometric centrobaric coordinate of the geometric features as matching point.For point feature, feature’s Vector will be created by the combination of region direction of modulus. For geometric feature, feature’s Vector is its perimeter and area information. At last, the key points between images will be associated by Maximum Common Subgraph method and validated by the feature vectors. The experimental results show that the methods are reliable and robust in continuous sonar image of seafloor.
Index Terms:
Sonar Image Feature, MMWT, Maximum Common Subgraph, Matching
Citation:
Shoudong Shi, Demin Xu, "A Robust Approach of Sonar Image Feature Detection and Matching," csie, vol. 6, pp.523-527, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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